SUMMARY ? PROJECT 1 Small cell lung cancer (SCLC) is a highly aggressive, incurable tumor. SCLC phenotypic heterogeneity has been associated with disease aggressiveness, yet there have been no clinical advances based on patient tumor stratification, and the uniform standard-of-care, based on combination chemo-radiation therapy unchanged for over half a century, remains largely ineffective. Recently, several groups including ourselves have independently identified phenotypic cell subpopulations in SCLC across a variety of experimental systems including human cell lines, patient-derived xenografts and primary tumors, as well as tumors from SCLC genetically engineered mouse models (GEMMs). Yet, there is no global understanding of SCLC phenotypic diversity across systems that could enable integration of findings, leverage GEMMs for translational purposes, and produce insights into its impact on treatment evasion. In this Project, we propose to address this challenge by developing a global blueprint of SCLC phenotypic space, clarifying the bias imposed to this space by genomic alterations, and understanding phenotype transition or selection dynamics in response to drugs. In Aim 1 we develop a workflow to infer SCLC phenotypic heterogeneity from bulk-level transcriptomics data, which we then validate experimentally at the single-cell level. We define a gene ontology metric to identify biological similarities and differences between phenotypes across model systems. The resulting phenotype map will inform studies aimed at connecting model systems to patients. In Aim 2, we propose to link the SCLC phenotypic heterogeneity space to genomic alterations, by statistical correlations validated with experiments that mechanistically induce cells to switch phenotypes through gene manipulation. Since in the clinic SCLC biopsies or surgery are rarely performed beyond initial diagnosis, we then propose liquid biopsies of circulating, cell-free DNA as a clinical proxy for the primary tumor, allowing a connection between these genomic alterations and phenotypic diversity of SCLC tumors. By bridging this gap, predictions about patient response to specific treatments could eventually be made. In Aim 3, we investigate the relative role of transitions vs. selection in supporting SCLC phenotypic plasticity and drug treatment evasion. To this end, we use DNA barcoding and information theory techniques to quantify rates of diversification of SCLC phenotypes in response to drug treatment. Specifically, we map trajectories of cells within the SCLC phenotype space as cells adapt and evade treatment. In summary, we propose to develop a comprehensive view of SCLC phenotypic heterogeneity, linking transcriptomic, genomic, and functional features of SCLC cells across diverse experimental model systems and patient primary tumor specimens. We will link these observations to clinically measurable variables, and develop a unified map of phenotypic response dynamics in response to therapy, providing possible novel avenues to SCLC treatment strategies.